89,342 research outputs found
Two-Stream RNN/CNN for Action Recognition in 3D Videos
The recognition of actions from video sequences has many applications in
health monitoring, assisted living, surveillance, and smart homes. Despite
advances in sensing, in particular related to 3D video, the methodologies to
process the data are still subject to research. We demonstrate superior results
by a system which combines recurrent neural networks with convolutional neural
networks in a voting approach. The gated-recurrent-unit-based neural networks
are particularly well-suited to distinguish actions based on long-term
information from optical tracking data; the 3D-CNNs focus more on detailed,
recent information from video data. The resulting features are merged in an SVM
which then classifies the movement. In this architecture, our method improves
recognition rates of state-of-the-art methods by 14% on standard data sets.Comment: Published in 2017 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS
The role of spatial frequency information for ERP components sensitive to faces and emotional facial expression
To investigate the impact of spatial frequency on emotional facial expression analysis, ERPs were recorded in response to low spatial frequency (LSF), high spatial frequency (HSF), and unfiltered broad spatial frequency (BSF) faces with fearful or neutral expressions, houses, and chairs. In line with previous findings, BSF fearful facial expressions elicited a greater frontal positivity than BSF neutral facial expressions, starting at about 150 ms after stimulus onset. In contrast, this emotional expression effect was absent for HSF and LSF faces. Given that some brain regions involved in emotion processing, such as amygdala and connected structures, are selectively tuned to LSF visual inputs, these data suggest that ERP effects of emotional facial expression do not directly reflect activity in these regions. It is argued that higher order neocortical brain systems are involved in the generation of emotion-specific waveform modulations. The face-sensitive N170 component was neither affected by emotional facial expression nor by spatial frequency information
DeeperCut: A Deeper, Stronger, and Faster Multi-Person Pose Estimation Model
The goal of this paper is to advance the state-of-the-art of articulated pose
estimation in scenes with multiple people. To that end we contribute on three
fronts. We propose (1) improved body part detectors that generate effective
bottom-up proposals for body parts; (2) novel image-conditioned pairwise terms
that allow to assemble the proposals into a variable number of consistent body
part configurations; and (3) an incremental optimization strategy that explores
the search space more efficiently thus leading both to better performance and
significant speed-up factors. Evaluation is done on two single-person and two
multi-person pose estimation benchmarks. The proposed approach significantly
outperforms best known multi-person pose estimation results while demonstrating
competitive performance on the task of single person pose estimation. Models
and code available at http://pose.mpi-inf.mpg.deComment: ECCV'16. High-res version at
https://www.d2.mpi-inf.mpg.de/sites/default/files/insafutdinov16arxiv.pd
Elderly Fall Detection Systems: A Literature Survey
Falling is among the most damaging event elderly people may experience. With the ever-growing aging population, there is an urgent need for the development of fall detection systems. Thanks to the rapid development of sensor networks and the Internet of Things (IoT), human-computer interaction using sensor fusion has been regarded as an effective method to address the problem of fall detection. In this paper, we provide a literature survey of work conducted on elderly fall detection using sensor networks and IoT. Although there are various existing studies which focus on the fall detection with individual sensors, such as wearable ones and depth cameras, the performance of these systems are still not satisfying as they suffer mostly from high false alarms. Literature shows that fusing the signals of different sensors could result in higher accuracy and lower false alarms, while improving the robustness of such systems. We approach this survey from different perspectives, including data collection, data transmission, sensor fusion, data analysis, security, and privacy. We also review the benchmark data sets available that have been used to quantify the performance of the proposed methods. The survey is meant to provide researchers in the field of elderly fall detection using sensor networks with a summary of progress achieved up to date and to identify areas where further effort would be beneficial
Rapid Visual Categorization is not Guided by Early Salience-Based Selection
The current dominant visual processing paradigm in both human and machine
research is the feedforward, layered hierarchy of neural-like processing
elements. Within this paradigm, visual saliency is seen by many to have a
specific role, namely that of early selection. Early selection is thought to
enable very fast visual performance by limiting processing to only the most
salient candidate portions of an image. This strategy has led to a plethora of
saliency algorithms that have indeed improved processing time efficiency in
machine algorithms, which in turn have strengthened the suggestion that human
vision also employs a similar early selection strategy. However, at least one
set of critical tests of this idea has never been performed with respect to the
role of early selection in human vision. How would the best of the current
saliency models perform on the stimuli used by experimentalists who first
provided evidence for this visual processing paradigm? Would the algorithms
really provide correct candidate sub-images to enable fast categorization on
those same images? Do humans really need this early selection for their
impressive performance? Here, we report on a new series of tests of these
questions whose results suggest that it is quite unlikely that such an early
selection process has any role in human rapid visual categorization.Comment: 22 pages, 9 figure
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